Method and system for correcting artifacts in image reconstruction

ABSTRACT

Methods and systems are provided for correcting artifacts in iterative reconstruction processes. In certain embodiments, weighting schemes may be applied such that less than all of the available scan or projection data is utilized in the iterative reconstruction. In this manner, inconsistencies in the data undergoing reconstruction may be reduced.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a Divisional Application of U.S. application Ser.No. 12/640,936, entitled “METHOD AND SYSTEM FOR CORRECTING ARTIFACTS INIMAGE RECONSTRUCTION”, filed Dec. 17, 2009, which is herein incorporatedby reference in its entirety.

BACKGROUND OF THE INVENTION

The subject matter disclosed herein relates to non-invasive imaging and,in particular, to correcting or reducing artifacts in the imagereconstruction process.

In the fields of medical imaging, animal imaging, quality control andsecurity screening, non-invasive imaging techniques have gainedimportance due to benefits that include unobtrusiveness, convenience,and speed. In medical and research contexts, non-invasive imagingtechniques are used to image organs or tissues beneath the surface ofthe skin. Similarly, in industrial or quality control (QC) contexts,non-invasive imaging techniques are used to examine parts or items forhidden defects that may not be evident from an external examination. Insecurity screening, non-invasive imaging techniques are typically usedto examine the contents of containers (e.g., packages, bags, or luggage)without opening the containers and/or to screen individuals entering orleaving a secure location.

A number of non-invasive imaging modalities exist today. A particularmodality may be selected based upon the imaging context, such as theorgan or tissue to be imaged, the spatial and/or temporal resolutiondesired, or upon whether structural or functional characteristics are ofinterest. One type of imaging modality is computed tomography (CT) inwhich X-ray attenuation data is collected at different angular positionsrelative to the subject or object undergoing imaging. The collected datais reconstructed to generate three-dimensional representations of thesubject or object undergoing imaging, including those internalstructures not typically visible in an external examination. Onetechnique by which image data may be reconstructed is iterativereconstruction, which may be utilized when it is desired to optimizeimage quality and minimize patient dose.

However, the iterative reconstruction process may be susceptible tovarious undesired image artifacts in the generated image. Theseartifacts may arise from a number of sources, including motion of orwithin the subject being imaged, data inconsistencies introduced by thescan protocol, and/or data inconsistencies introduced by thereconstruction technique. The artifacts may degrade the image qualityand/or reduce the usefulness of the images. However, even if theartifacts do not reduce the usefulness of the images, their presence isstill typically undesirable as they detract from the image quality. Insome instances, iterative reconstruction methods may be as or moresusceptible to such artifacts than direct reconstruction techniques,such as filtered backprojection (FBP).

BRIEF DESCRIPTION OF THE INVENTION

In an embodiment, an image reconstruction method is provided. Inaccordance with this embodiment, one or more sets of scan data acquiredusing a computed tomography system are accessed. A voxel-dependentangular weighting function corresponding to the one or more sets of scandata is computed. The respective weighting function is used toiteratively compute a set of images from the sets of scan data, suchthat the angular range of the data contributing to each voxel is mademore uniform and/or the angular range is narrowed for one or morevoxels.

In one embodiment, an image reconstruction method is provided. Inaccordance with this embodiment, one or more sets of projection dataacquired using a computed tomography system are accessed. Adetector-channel dependent projection-domain weighting corresponding tothe one or more sets of projection data is computed such that thecontribution from one or more detector regions is reduced. Thedetector-channel dependent projection-domain weighting is applied toiteratively reconstruct one or more images

In another embodiment, an image reconstruction method is provided. Inaccordance with this embodiment, inconsistent regions of a sinogram usedin a model-based iterative reconstruction are identified. Theinconsistent regions are determined based on the magnitude of an errorsinogram after a number of iterations. A filtered backprojection (FBP)is performed to reconstruct an FBP image. The FBP image is reprojectedto generate all or part of a reprojected sinogram. Portions of thereprojected sinogram corresponding to the identified inconsistentregions are substituted in the sinogram to generate a blended sinogram.The blended sinogram is iteratively reconstructed to reconstruct one ormore images

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features, aspects, and advantages of the presentinvention will become better understood when the following detaileddescription is read with reference to the accompanying drawings in whichlike characters represent like parts throughout the drawings, wherein:

FIG. 1 is a diagrammatical illustration of an exemplary CT imagingsystem, in accordance with an embodiment of the present disclosure;

FIG. 2 depicts a view-dependent weighting performed on a set of helicalscan data to generate more uniform view contributions for each voxel, inaccordance with one embodiment of the present disclosure;

FIG. 3 depicts a geometrical description of a Tam window associated witha helical scan, in accordance with one embodiment of the presentdisclosure;

FIG. 4 depicts the amount of projection data available using differentwindow functions based on the Tam window, in accordance with oneembodiment of the present disclosure;

FIG. 5 depicts three variant Tam windows that are offset from oneanother along the row direction, in accordance with one embodiment ofthe present disclosure; and

FIG. 6 depicts a flowchart of steps for generating a blended sinogram ofcorrected projection data, in accordance with one embodiment of thepresent disclosure.

DETAILED DESCRIPTION OF THE INVENTION

The embodiments discussed below describe various approaches for reducingartifacts using iterative image reconstruction techniques to reconstructtomographic image data. In general, these approaches reduce artifactsthat are attributable to inconsistencies present in the data inmodel-based statistical iterative reconstruction (MBIR). Suchinconsistencies may be present due to motion of the subject or motionwithin the region undergoing imaging and/or due to geometric constraintsassociated with the scan protocol. Before a detailed discussion of thesystem and methods are described in accordance with various embodimentsof the present technique, it may be beneficial to discuss embodiments ofimaging systems that may be suitable for performing the methodsdescribed herein.

Turning now to the figures, FIG. 1 is a diagram that illustrates animaging system 10 for acquiring and processing image data. In theillustrated embodiment, system 10 is a CT system designed to acquireX-ray projection data, to reconstruct the projection data into atomographic image, and to process the image data for display andanalysis, in accordance with the present technique. Though the imagingsystem 10 is discussed in the context of medical imaging, the techniquesand configurations discussed herein are applicable in other non-invasiveimaging contexts, such as security screening or industrialnondestructive evaluation of manufactured parts. In the embodimentillustrated in FIG. 1, the CT imaging system 10 includes an X-ray source12. As discussed in detail herein, the source 12 may include one or moreconventional X-ray sources, such as an X-ray tube, or a distributedsource configured to emit X-rays from different locations along asurface. For example, the source 12 may include one or more addressablesolid-state emitters. Such solid-state emitters may be configured asarrays of field emitters, including one-dimensional arrays, i.e., lines,and two-dimensional arrays.

The source 12 may be positioned proximate to a collimator 14. Thecollimator 14 may consist of one or more collimating regions, such aslead or tungsten shutters, for each emission point of the source 12. Thecollimator 14 typically defines the size and shape of the one or moreX-ray beams 16 that pass into a region in which a subject 18, such as ahuman patient, is positioned. Each X-ray beam 16 may be generallyfan-shaped or cone-shaped, depending on the configuration of thedetector array and/or the desired method of data acquisition, asdiscussed below. An attenuated portion 20 of each X-ray beam 16 passesthrough the subject 18 and impacts a detector array, representedgenerally at reference numeral 22.

The detector 22 is generally formed by a plurality of detector elementsthat detect the X-ray beams 16 after they pass through or around thesubject 18. Each detector element produces an electrical signal thatrepresents the intensity of the X-ray beam 16 incident at the positionof the detector element when the beam strikes the detector 22.Alternatively, each element of detector 22 may count incident photons inthe X-ray beam 16 and may also determine their energy. Typically, theX-ray beam 16 is generated and the corresponding electrical signals areacquired at a variety of angular positions around the subject ofinterest so that a plurality of radiographic projection views can becollected. The electrical signals are acquired and processed toreconstruct an image that is indicative of the features within thesubject 18, as discussed in further detail below.

A system controller 24 commands operation of the imaging system 10 toexecute examination protocols and to process the acquired data. Thesource 12 is typically controlled by a system controller 24. Generally,the system controller 24 furnishes power, focal spot location, controlsignals and so forth, for the CT examination sequences. The detector 22is coupled to the system controller 24, which commands acquisition ofthe signals generated by the detector 22. The system controller 24 mayalso execute various signal processing and filtration functions, such asinitial adjustment of dynamic ranges, interleaving of digital imagedata, and so forth. In the present context, system controller 24 mayalso include signal-processing circuitry and associated memorycircuitry. As discussed in greater detail below, the associated memorycircuitry may store programs and/or routines (such as programs and/orroutines suitable for correcting or reducing artifacts in an iterativereconstruction implementation, as disclosed herein) executed by thesystem controller 24 or a processor-based system in communication withthe system controller 24. Further, the memory circuitry of the systemcontroller 24 may also store configuration parameters, image data, andso forth. In one embodiment, the system controller 24 may be implementedas all or part of a processor-based system such as a general purpose orapplication-specific computer system.

In the illustrated embodiment of FIG. 1, the system controller 24 maycontrol the movement of a linear positioning subsystem 28 and arotational subsystem 26 via a motor controller 32. In an embodimentwhere the imaging system 10 includes rotation of the source 12 and/orthe detector 22, the rotational subsystem 26 may rotate the source 12,the collimator 14, and/or the detector 22 about the subject 18. Itshould be noted that the rotational subsystem 26 might include a gantrycomprising both stationary components (stator) and rotating components(rotor). The linear positioning subsystem 28 may enable the subject 18,or more specifically a patient table that supports the subject 18, to bedisplaced linearly. Thus, the patient table may be linearly moved withinthe gantry or within an imaging volume (e.g., the volume located betweenthe source 12 and the detector 22) and enable the acquisition of datafrom particular areas of the subject 18 and, thus the generation ofimages associated with those particular areas. Additionally, the linearpositioning subsystem 28 may displace the one or more components of thecollimator 14, so as to adjust the shape and/or direction of the X-raybeam 16. In embodiments comprising a stationary source 12 and astationary detector 22, a mechanical rotational subsystem may be absent,with emitters spaced at different angular locations about the subjectinstead being activated at different times to allow acquisition ofprojections at different angles. Similarly, in embodiments in which thesource 12 and the detector 22 are configured to provide extended orsufficient coverage along the z-axis (i.e., the axis associated with themain length of the subject 18) and/or linear motion of the subject isnot required, the linear positioning subsystem 28 may be absent.

The source 12 may be controlled by an X-ray controller 30 disposedwithin the system controller 24. The X-ray controller 30 may beconfigured to provide power and timing signals to the source 12. Inaddition, in some embodiments the X-ray controller 30 may be configuredto selectively activate the source 12 such that tubes or emitters atdifferent locations within the system 10 may be operated in synchronywith one another or independent of one another.

Further, the system controller 24 may comprise a data acquisition system34. In such an embodiment, the detector 22 is coupled to the systemcontroller 24, and more particularly to the data acquisition system 34.The data acquisition system 34 receives data collected by readoutelectronics of the detector 22. The data acquisition system 34 typicallyreceives sampled analog signals from the detector 22 and converts thedata to digital signals for subsequent processing by a processor-basedsystem, such as a computer 36. Alternatively, in other embodiments, thedetector 22 may convert the sampled analog signals to digital signalsprior to transmission to the data acquisition system 34.

In the depicted embodiment, a computer 36 is coupled to the systemcontroller 24. The data collected by the data acquisition system 34 maybe transmitted to the computer 36 for subsequent processing andreconstruction. For example, the data collected from the detector 22 mayundergo pre-processing and calibration at the data acquisition system 34and/or the computer 36 to produce representations of the line integralsof the attenuation coefficients of the subject 18 and the scannedobjects. In one embodiment, the computer 36 contains image-processingcircuitry 37 for processing and filtering the data collected from thedetector 22. The processed data, commonly called projections, may thenbe reconstructed by the image processing circuitry 37 to form an imageof the subject 18 and/or the scanned area. In one implementation, theprojections are reconstructed into an image by using one or morereconstruction algorithms, such as in accordance with an iterativereconstruction technique as discussed herein. Once reconstructed, theimage produced by the system 10 of FIG. 1 may reveal an internal regionof interest of the subject 18 which can be used for diagnosis,evaluation, and so forth.

The computer 36 may comprise or communicate with a memory 38 that canstore data processed by the computer 36, data to be processed by thecomputer 36, or routines and/or algorithms to be executed by thecomputer 36, such as for processing image data in accordance with thepresent technique. It should be understood that any type of computeraccessible memory device capable of storing the desired amount of dataand/or code may be utilized by such a system 10. Moreover, the memory 38may comprise one or more memory devices, such as magnetic, solid-state,or optical devices, of similar or different types, which may be localand/or remote to the system 10. The memory 38 may store data, processingparameters, and/or computer programs comprising one or more routines oralgorithms for performing the iterative reconstruction and/or theartifact correction processes described herein.

The computer 36 may also be adapted to control features enabled by thesystem controller 24 (i.e., scanning operations and data acquisition).Furthermore, the computer 36 may be configured to receive commands andscanning parameters from an operator via an operator workstation 40which may be equipped with a keyboard and/or other input devices. Anoperator may, thereby, control the system 10 via the operatorworkstation 40. Thus, the operator may observe from the computer 36 thereconstructed image and other data relevant to the system 10, initiateimaging, select and apply image filters, and so forth. Further, theoperator may manually identify and/or review features and regions ofinterest from the reconstructed image. Automated detection algorithmsmay be applied to aid in identifying and/or manipulating the features orregions of interest.

As illustrated, the system 10 may also include a display 42 coupled tothe operator workstation 40. The display 42 may be utilized to observethe reconstructed images, for instance. Additionally, the system 10 mayinclude a printer 44 coupled to the operator workstation 40 andconfigured to print a copy of the one or more reconstructed images. Thedisplay 42 and the printer 44 may also be connected to the computer 36directly or via the operator workstation 40. Further, the operatorworkstation 40 may include or be coupled to a picture archiving andcommunications system (PACS) 46. It should be noted that PACS 46 mightbe coupled to a remote system 48, radiology department informationsystem (RIS), hospital information system (HIS) or to an internal orexternal network, so that others at different locations can gain accessto the image data.

Although only one operator workstation is depicted, one or more operatorworkstations 40 may be linked in the system 10 for outputting systemparameters, requesting examinations, viewing images, and so forth. Ingeneral, displays 42, printers 44, workstations 40, and similar devicessupplied within the system 10 may be local to the data acquisitioncomponents, or may be remote from these components, such as elsewherewithin an institution or hospital, or in an entirely different location,linked to the image acquisition system 10 via one or more configurablenetworks, such as the Internet, virtual private networks, and so forth.

Although the previous discussion discloses typical embodiments of theimaging system 10, other system configurations may be employed toacquire image data. The image reconstruction process performed on animaging system 10 is often based on filtered backprojection techniques.The filtered backprojection technique generally involves the steps ofweighting, filtering, and backprojecting the acquired sinogram data. Asmay be appreciated, the sinogram is a representation of the datacollected by the data acquisition system 34 of FIG. 1. In particular,for single-row detectors, the sinogram is a two-dimensional dataset,p(s,θ), obtained by stacking the one-dimensional projections, p_(θ)(s),where θ is the view angle of data acquisition, and s is the detectorelement. For multi-row detectors, the sinogram becomes athree-dimensional dataset. Some of the techniques described herein maybe discussed in the context of a two dimensional sinogram forsimplicity, but the application to three dimensions is straightforward,if not identical. Thus, the sinogram is a collection of output data fromthe detector array 22 resulting from radiation traversing the subject ofinterest 18 at a given source position. The output data from each sourceand detector position or view corresponds to a row of projection data inthe sinogram. Thus, each row of the sinogram constitutes a projectionview that is indicative of the attenuation information for a distinctview angle, for given source and detector positions, with respect to thesubject 18.

Except for objects lying at the center of the CT system 10, allattenuating objects (e.g., tissue, bone, contrast agent, and so forth)within the field of view will appear in the sinogram as a sine-likewave, whose position corresponds to their location in the subject ofinterest. That is, the location of particular data resulting fromattenuation by an object or region may appear as a distinguishablesinusoidal trace. Thus, for a static point in the imaging area and aparallel beam X-ray source, the sinogram does indeed possess asinusoidal form. However, in the presence of motion within the imagingarea, the sinogram or portions of the sinogram will deviate from thesinusoidal form.

In accordance with a filtered backprojection reconstruction, theweighting of the sinogram data may involve point-by-point multiplicationby a pre-calculated 2D array (for single-row detector systems) or 3Darray (for multi-row detector systems). The filtering or convolutionstep filters the sinogram data to decorrelate them and may be carriedout as a series of one-dimensional convolutions. In the backprojectionstep, the measured sinogram data is added to all picture elements in animage along the projection lines of the original projection views.

While such filtered backprojection reconstructions are computationallyfast to perform, other reconstruction techniques may also be performedand may be favored for various reasons, such as image quality, noisereduction, increased spatial resolution, suitability with incompletedata, low-dose suitability, and so forth. For example, iterativereconstruction techniques (such as model-based iterative reconstruction(MBIR)) may be employed to reconstruct the projection data into usefulimages. Iterative image reconstruction techniques utilize a variety ofalgorithms that incorporate various assumptions or expectations withrespect to the acquired image data. For example, an idealized orexpected image for the anatomy of interest may be modeled (taking intoaccount system geometry and settings, scan protocol, and so forth) togenerate a reconstruction model that is used in the reconstructionprocess. Likewise, a system model (i.e., a geometric and physicalrepresentation of the imaging system during image data acquisition) maybe employed to relate the image space to the projection space via anumber of computed coefficients for each voxel/view pair. Projectionsbased on the reconstruction model may be computed (using the systemmodel) and compared to the actual acquired image data using statisticalmodeling techniques. The differences between the acquired image data andthe reconstruction model may be used to update or incrementally modifythe generated (i.e., backprojected) image. This process may be repeatedfor a set number of iterations, until the differences are below somethreshold value (i.e., convergence), or until the criteria of agoverning cost function are satisfied.

Iterative image reconstruction may be susceptible to inconsistencies inthe projection data relative to the reconstruction model, which maymanifest as artifacts in the reconstructed images. In particular, theiterative reconstruction algorithm may try to converge on the image thatbest explains the inconsistent measurements, which may result in imagescontaining artifacts. For example, CT images generated using iterativereconstruction techniques may be susceptible to artifacts related tomotion during acquisition of the image data, especially to the extentthat the reconstruction model does not explicitly account for suchmotion. Likewise, noise or poor calibration of the imaging system maylead to inconsistencies in the acquired image data that may result inimage artifacts. Further, if the data is acquired using a helical scanprotocol (i.e., linear displacement of the subject during rotation ofthe source and detector), the geometric characteristics of the helicalacquisition can result in data inconsistencies that generate imageartifacts.

In certain instances, an iterative reconstruction algorithm may enhancesuch artifacts when data inconsistencies are present. In particular,artifacts may be more pronounced in regions of stronger gradient of thebackprojection weight. That is, artifacts may be enhanced incircumstances where the image volume receives non-uniform contributionsfrom the system model during the backprojection process, due to somevoxels receiving contributions from more views than other voxels. Thus,the resulting artifacts may be stronger where the backprojectioncontribution varies more strongly from voxel to voxel.

Problems such as these that are associated with non-uniformcontributions in the backprojection may be present primarily in helicalscan acquisitions and iterative reconstruction implementations. Forexample, the quality of images generated using iterative reconstructionmay be degraded by rotational artifacts observed from slice to slice.These artifacts may manifest as shading and/or banding streaks in axialand reformat images. Likewise, artifacts attributable to patient orobject motion during data acquisition may result in streaks and shadingin images generated using iterative reconstruction techniques if themotion is not explicitly accounted for in the modeling process.

With the foregoing discussion in mind, the following approaches may beused, alone or in conjunction with one another or other techniques, toaddress artifacts generated using iterative reconstruction algorithms.For example, in one implementation, a voxel-dependent angular weightingfunction may be employed to modulate non-uniformities in the number ofviews contributing to each voxel. In particular, in calculating theupdate for a current voxel, iterative reconstruction techniques willtypically utilize all available image data. As noted above, such anapproach may be subject to artifacts due to the large differences in theamount of data (e.g., views) contributing to each voxel.

To reduce or eliminate these artifacts, a specific angular weighting(e.g., a view dependent weighting) may be applied to the datacontributing to each voxel so that the amount of data used for eachvoxel becomes more uniform, thereby reducing artifacts. In certainimplementations, the weighting function may be a trapezoidal-shapedweighting function or a rectangular-shaped weighting function (which isequivalent to modifying the angular range). For example, referring toFIG. 2, an initial set 60 of helical scan data (acquired at a pitch of1.0) illustrates the number of views contributing to each voxel. Asdepicted by the sharp drop-offs 62, there may be abrupt and sharptransitions in the data set 60 where the number of views contributing toproximate voxels changes. Such abrupt changes or drop-offs may beassociated with artifacts in the reconstructed images. As depicted inFIG. 2, a view-dependent weighting function 66 may be defined for theinitial scan data 60. In the depicted example, the view dependentweighting function 66 increases the uniformity of the amount of dataused for each voxel. This increased uniformity of the amount of dataused for each voxel (as depicted in weighted data set 70) may result infewer and/or less severe artifacts in the images generated usingiterative reconstruction techniques.

By way of example, for plane z=0 and considering all point x,y in thatplane, in a helical scan there can be one or more continuous segments ofviews that contribute to a given voxel. Typically there may be a singlecontinuous segment, but there may be more than one continuous segmentdepending on the pitch associated with the data acquisition. The totalnumber of views available for voxel (x,y), i.e., Tol_Ang (x,y) is equalto:

$\begin{matrix}\begin{matrix}{{{Tol\_ Ang}\left( {x,y} \right)} = {\sum\limits_{i}^{\;}\left( {{{Ang\_ e}_{i}\left( {x,y} \right)} - {{Ang\_ b}_{i}\left( {x,y} \right)}} \right)}} \\{= {{length}\left( {{Ang\_ vec}\left( {x,y} \right)} \right)}}\end{matrix} & (1)\end{matrix}$

where [Ang_b_(i)(x, y), Ang_e_(i)(x, y)) is the ith continuous viewsegment and where

${{Ang\_ vec}\left( {x,y} \right)} = {\bigcup\limits_{i}{\left\lbrack {{{Ang\_ b}_{i}\left( {x,y} \right)},{{Ang\_ e}_{i}\left( {x,y} \right)}} \right).}}$

As noted above, Tol_Ang(x, y) may not be a smooth function of (x,y), asdemonstrated by data set 60 of FIG. 2. To smooth the variation in theamount of data contributing to each voxel in plane (x,y) (i.e., toremove or reduce the sharp drop-offs 62 of FIG. 2) a smoothing functionmay be applied (i.e., diffusion, low pass filtering, and so forth) togenerate Tol_Ang(x, y)

An angular weighting function can then be defined such that thecontribution to voxel (x,y) is scaled by Wght(x, y) so that:

{tilde over (T)}ol_Ang(x, y)=Ang_vec(x, y)·Wght(x, y)′  (2)

The angular range modulation function defined by Equation (2) will beused as a weighting on the projection data before or duringbackprojection in the iterative reconstruction process. In oneembodiment, the weight provided by the angular modulation function isonly involved in calculating the update for the backprojection step.

A similar approach may also be useful in reducing motion artifacts. Inone such implementation, instead of smoothing the amount of data(angular range) for each voxel, as described above, a smaller angularrange, i.e., less data, may be used to improve the temporal resolutionof the iterative reconstruction. In one embodiment, this may beaccomplished by applying an angle-dependent weighting. For example, amaximum weight of 1.0 may be applied at the center view for a voxel andthis weight may be tapered down to 0.0 for the views furthest away fromthe center view.

In one embodiment, to minimize the loss of data and preserve the noisebenefits of iterative reconstruction, the narrowing of the angular rangemay be applied to voxels identified as being subject to motion. Forexample, to identify voxels where motion appears to be occurring, theresidual error sinogram of the iterative reconstruction may be analyzed.As will be appreciated, larger errors may be observed in the residualerror sinogram to the extent such motion is not explicitly accounted forby the model. Regions of motion thus identified may then be subjected toa narrowing of the angular range for the relevant voxels as discussedabove to effectively increase the temporal resolution for those voxelssubject to motion. Alternatively, in another embodiment, separatereconstructions can be performed for those regions of the image subjectto motion and for those regions not subject to motion, with the separatereconstructions having different shifts in the centers of the angularranges. For the regions of the image subject to motion, the density ofthose voxels will vary based on the respective reconstruction.

One consequence of the use of a view dependent weighting scheme, asdiscussed herein, may be that different voxels are updated usingdifferent cost functions. However, it is also possible that theview-dependent weighting function can be tuned for each voxel toeliminate or reduce the computational impact of using multiple costfunctions for image convergence or to otherwise improve image quality ona voxel-by-voxel basis.

In addition to such view or angle-dependent weighting schemes, otherartifact reduction implementations are presently contemplated. Forexample, in one embodiment, a projection domain weighting may be applied(which may be voxel independent and/or view independent). Unlike thepreviously described view or angle-dependent weighting schemes, suchprojection domain weighting approaches do not modulate the amount ofdata on a voxel by voxel basis. Instead, a smoothed detector-weightingwindow can be applied to all the projections in addition to the originalstatistical weighting applied in iterative reconstruction. The effect ofthis detector-weighting function is to provide a lower weight to some orall of the projection data corresponding to specific regions on thedetector. Thus, in such an approach, instead of discarding data on aview or angle basis, data within the projection domain may instead besacrificed to reduce or eliminate data inconsistencies that may lead toreconstruction artifacts. In general, any weighting function can beused. In other words, the statistical weights used typically in theiterative reconstruction algorithm will be augmented withdetector-channel dependent weights, which depend on the geometricalaspects of the scanner and the scan protocol, to enhance uniformity inthe backprojection.

For example, in one embodiment the Tam window is used as the detectorweighting function. The Tam window is the minimum data needed toreconstruct a voxel in a helical scan and is known in analytic (i.e.,non-iterative) reconstruction theory. In particular, the Tam window isthe region bounded, in the detector, by the cone-beam projection of theupper and lower turns of the helix. An example of a Tam window and itsgeometrical definition are provided in FIG. 3. As depicted, the upperand lower boundaries 80 may be obtained by looking from the source 12and projecting the helical trajectory onto the detector 22. In anembodiment in which the Tam window is used as the detector weightingfunction, the total backprojection contribution to different voxels(i.e., the total number of views contributing to each voxel) changesvery smoothly. One side effect of using the Tam window as the weightingfunction in iterative reconstruction is that data in the corners of thedetector 22 may be unused, which may affect dose efficiency and noiselevel in the images.

With respect to artifact reduction as discussed herein, the goal is toachieve a smoothly varying contribution in the backprojection whilemaintaining relatively low noise levels. In one embodiment, the Tamwindow may be artificially enlarged and the edges of the Tam windowfeathered to generate a larger and smoother window function in thedetector domain. Such modifications of the Tam window may yield asuitable tradeoff between increased noise and lower artifacts. Other, adhoc detector window functions may also be applied (such as rectangularor trapezoidal window functions) with or without feathering.

For example, turning to FIG. 4, the amount of data from different windowfunctions based on the Tam window is depicted. In particular, thedifferent graphs of FIG. 4 depict the change and the uniformity of thesystem model data for the different window functions. In FIG. 4, adepiction of the projection data used in a conventional iterativereconstruction, which uses 100% of the projection data, is depicted bygraph 90. As evidenced by the surface of the depicted projection data ofgraph 90, the projection data used is not uniform, leading to steepgradients and surface irregularities. Likewise, graph 92 depictsprojection data used when an unmodified Tam window is applied as adetector weighting function, i.e., when the minimum data (approximately50% of the projection data) needed to reconstruct a voxel in a helicalscan (pitch=1.0) is used. As evidenced by the surface of the depictedprojection data of graph 90, the projection data used is generallyuniform, with only a weak gradient and few or no surface irregularities.Graphs 94, 96, 98 depict the percent of projection data used, and itscorresponding uniformity, when modified Tam windows (i.e., enlarged Tamwindows and/or Tam windows with feathered edges) are applied as adetector weighting function. As depicted, the modified Tam windows mayallow the use of more projection data while still reducing the gradientand improving the uniformity of the projection data relative to theconventional iterative reconstruction (depicted by graph 90). Asdepicted by graphs 92, 94, 96, 98, by using a weighted window functionand less than 100% of the projection data, the data surface may besmoothed such that irregularities and steep gradients in theconventional reconstruction data (depicted by graph 90) may be removedor reduced, thereby reducing potential data inconsistencies andartifacts in the reconstructed images.

For example, for a cylindrical detector the upper and lower boundariesof the Tam window are defined as:

B⁺(α), B⁻(α)   (3)

respectively, where α is the fan angle in the x-y plane. In accordancewith this window function, values within the window are weighted by avalue of 1 (i.e., given their full value); values outside the window areweighted by a value of 0 (i.e., given no weight). An extended orenlarged Tam window may be defined as:

B⁺(α)+k, B⁻(α)−k   (4)

where k is a small shift to make a larger window to allow the use ofmore data in the iterative reconstruction process. As noted above, somefeathering may also be applied at the shifted Tam window boundaries tomake a smooth transition. Thus, due to the feathering, values within thewindow are weighted by a value of 1 (i.e., given their full value);values outside the window are weighted by a value of 0 (i.e., given noweight), and values at the boundary of the window are given a weightbetween 0 and 1 such that a smooth transition occurs at the edge of thewindow between full weighting and no weighting. In one embodiment, theseweights (i.e., 0, 1, and between 0 and 1 at the window boundary) aremultiplied with the original iterative reconstruction statisticalweights prior to backprojection.

In certain implementations, artificial data outside the real detectorboundary may be synthesized to make the detector artificially larger. Insuch an implementation, the weighting function (e.g., window) may bemade even larger and may allow the use of more real data. The boundaryof the window function may be pushed out further (i.e., larger k) suchthat a larger fraction of the actual measured data receives a non-zeroweight. The synthetic data may be generated using extrapolation and/orthrough reprojection of intermediate images. Such an approach mayeliminate the noise penalty of other approaches but may slightlyincrease reconstruction time due to the generation of and use of thesynthesized data points used in the reconstruction.

The projection-based approaches discussed herein may also providebenefits with respect to motion artifact reduction in two ways. First,the smooth transition of the window may lessen streaks attributable tomotion. Second, a smaller temporal window (i.e., higher temporalresolution) may be utilized to keep the consistency in the movingsinogram as high as possible. For example, the moving portion of thesinogram may be weighted downward (i.e., given less weight), giving lessemphasis to that portion of the data when calculating updates for thenext steps or iterations. Thus, the artifacts caused by theinconsistencies in the sinogram that are attributable to motion may bereduced.

While the preceding approaches reduce data utilization, and may therebyincrease noise in the reconstructed images, other approaches may avoidthese issues. For example in one embodiment, multiple shifted versionsof overlapping or proximate images may be reconstructed, using the oneor both of the preceding approaches, and combined to form a diagnosticimage. In a view or angular weighting implementation, the view weightingintervals are shifted in the view (i.e., time) direction to generatetemporally offset images. In a projection-weighted implementation, theprojection weighting can be shifted in the detector row (i.e., time)direction to generate temporally offset images. For example, referringto FIG. 5, three different Tam windows 110, 112, 114 are depicted whichare offset (offsets 116 and 118) from one another in the row (time)direction. In one embodiment, the three temporally offset Tam windows110, 112, 114 cover a full 360° or more in the aggregate, though theindividual windows each cover less than the full 360°. The shiftedimages, such as the offset images depicted in FIG. 5, can be averaged orcombined in generating the final diagnostic image.

Such an approach results in a reconstruction using a smaller temporalwindow, which may be suitable for a reconstruction where motion ispresent. As noted above, each temporally offset window has less than afull 360° of data, such as 180° of data, but the temporally offsetwindows maybe combined to provide the full 360° or more of data. Thedata associated with each temporally offset window may be separatelyreconstructed, with each reconstruction being uncorrelated, i.e.,independent, of the others. In one embodiment, the same or differentFourier masks may be utilized in generating each separate image. Thefinal image may then be generated as an average of the separatereconstructions.

This approach provides two potential advantages. First, noise may bereduced by combining the information (or statistics) from the multipletemporally offset reconstructions. Second, artifacts may be reduced byaveraging or combining multiple images with different artifact content.

Further, this approach is suitable for use in model-based iterativereconstruction where large data inconsistencies are present, such as dueto motion by or in the subject being imaged. Instead of utilizing pure4D object reconstruction (such as utilizing motion vector estimationand/or motion compensation) a rough dynamic iterative reconstruction maybe used to approximate the motion effect on each voxel, i.e., the voxelvalue can change dynamically instead of being held to a constant value.For example, two or more separate reconstructions may be performed ondifferent temporal windows to reconstruct an image in two or more pointsin time (i.e., phases). A voxel representing a region where no motion ispresent will have the same value in the separate reconstructions, whilea voxel representing a region where motion is present will not have thesame value in the separate reconstructions, i.e., the voxel will changedensity in the different reconstructions. Further, the artifacts patternwill differ in the separate reconstructions. Therefore, in oneembodiment, combining the separate reconstruction images may result inthe same noise level (the non-motion region) and reduced artifacts(motion region).

The separate, offset reconstructed images can be combined by a varietyof techniques. For example, in one implementation, the separatereconstructions may be combined by averaging or adding the reconstructedimages. In another implementation, the separate reconstructions may becombined by computing a weighted combination based on the artifact level(i.e., the variance level). For example, greater weight may be given tothose reconstructed images with less artifacts, i.e., less variance. Ina further implementation, the separate reconstructions may be combinedby Fourier domain blending. As may be appreciated, streaks in thedifferent reconstructed images may correspond to different Fourierorientations and different Fourier masks may be used in the generationof each reconstructed image. In yet another implementation, the separatereconstruction may be combined by estimating and compensating relativeobject motion in the reconstructions prior to averaging.

While the preceding discussion described the separate and uncorrelated,i.e., independent, reconstruction of each offset window of data, inother embodiments the reconstruction processes may be correlated. Inparticular, it may be computationally efficient to jointly reconstructthe two or more sets of data with one objective function, such astemporally offset image or projection data. For example, to the extentthat a portion of the forward projection calculation may use the samegeometry for different offset windows, this calculation can be shared ina joint reconstruction.

In addition, joint reconstruction may provide image quality benefits,such as by regularizing the differences between the two states or, forexample, using a norm that encourages sparseness, such as the L1-norm.In particular, during the iteration process, information from otherreconstructions may be used to update or weight regions of greaterconsistency. For example, each reconstruction process may be related totry to minimize differences between the images reconstructed from thedifferent windows of data, i.e., to get a good correlation between thereconstructed images. In one embodiment, the differences between the tworeconstructions may be minimized, as the static regions should alreadybe similar or identical. For example, by doing a joint estimation of themultiple states, i.e., data windows, the voxels corresponding to regionsof motion may be identified due to their having more than one state.

In a further implementation, different weighting may be utilized ondifferent reconstruction regions. For example, more homogeneous regionsin the respective windows or sets of image data, i.e., regions havingfewer differences, may be more heavily weighted. Correspondingly, lesshomogeneous regions in the respective windows or sets of image data(i.e., regions having greater differences) may be more lightly weightedor may even be given a zero weight. Once the respective reconstructionsare generated for each respective window of image data, thereconstructions may be combined, such as using one or more of thetechniques described above.

In another embodiment, the backprojection can be modified further tomake the contribution to each voxel more uniform across x, y, z. Onesuch approach is to apply fan-to-parallel rebinning to the data. Inparticular, parallel beam (or cone-parallel) datasets do not have an 1/rdependence in the backprojector, where r represents the source-to-voxeldistance. Thus, in such an approach, conjugate views will have a moresimilar contribution, making the backprojection, and thus the updatestep, more uniform.

Alternatively, the backprojection weights may be explicitly modified toremove the 1/r dependence. In such an implementation, the backprojectorgeometrically stays in the original cone-beam geometry, but this iscompensated by modifying the backprojection weights by a factor:

r(x,y)/r_(—)0   (5)

such that the new backprojection weight in a given view is:

1/r(x,y)*r(x,y)/r _(—)0=1/r _(—)0   (6)

which is independent of (x,y). Thus, in such embodiments, the 1/rdependence may be compensated, thereby reducing or eliminating theeffects of the scanning geometry.

In another embodiment, the projection may be corrected to improveconsistency and to thereby improve the results of an iterativereconstruction process. Turning now to FIG. 6, in one such embodiment,the inconsistent projection data is identified (block 130), such as byidentifying portions of the error sinogram (i.e., measured sinogram 132)containing significant error, which likely corresponds to inconsistencyin the projection data. The inconsistent projection data thus identifiedmay be substituted (block 138) with synthesized data (i.e., reprojectedsinogram 136) from a filtered backprojection (FBP) reconstruction 134(which typically generates fewer motion artifacts than iterativereconstruction) performed on the same scan data (i.e., measured sinogram132). The substitution of the inconsistent projection data with thesynthesized projection data results in a set of blended data (i.e.,combined sinogram 140) that may be iteratively reconstructed (block 142)to generate reconstructions with fewer artifacts. That is, for regionsof the projection data containing motion artifacts, synthesizedprojection data is substituted to allow images to be iterativelyreconstructed with fewer motion artifacts.

In further embodiments, the cost function employed in the iterativereconstruction may be modified so as to reduce motion-based artifacts inthe reconstructed images. For example, in one embodiment, the costfunction may be modified to discount significant large errors in theerror sinogram attributable to inconsistencies in the projection data.The cost function typically increases or decreases in proportion to thepresence of a large residual error in the sinogram, e.g., the larger theresidual error in the sinogram, the greater the increase in the costfunction. In one implementation, a threshold may be established beyondwhich the cost function does not increase or decrease when the residualerror crosses the threshold. That is, the cost function may increase asthe residual error increase up to the point when the residual errorexceeds the established threshold. Once the residual error exceeds thethreshold, however, the cost function no longer increases. In accordancewith this approach, the effects of large errors, such as errorsassociated with motion, may be reduced or eliminated by limiting theadjustment made to the cost function by use of a threshold.

Technical effects of the invention include the generation of imagesusing iterative reconstruction techniques. Technical effects alsoinclude the generation of iteratively reconstructed images with reducedor corrected motion artifacts. Technical effects also include thegeneration of iteratively reconstructed images corrected for the effectsof a helical data acquisition. In certain embodiments, these technicaleffects may be achieved by using something less than all available scanor projection data for each voxel.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

1. An image reconstruction method, comprising the acts of: accessingprojection data acquired using a computed tomography system; computing adetector-channel dependent projection-domain weighting corresponding tothe projection data such that the contribution from one or more detectorregions is reduced and a weighting factor is the same across all of theprojection views; applying the detector-channel dependentprojection-domain weighting to iteratively reconstruct one or moreimages; and displaying one or more reconstructed images generated viaiterative reconstruction with the applied detector-channel dependentprojection-domain weighting.
 2. The image reconstruction method of claim1, wherein the detector-channel dependent projection domain weightingcomprises a Tam window or modified Tam window applied as a detectorweighting function.
 3. The image reconstruction method of claim 1,comprising supplementing the projection data with synthesized data. 4.The image reconstruction method of claim 1, wherein the projection datacomprise temporally offset projection data.
 5. The image reconstructionmethod of claim 4, comprising: independently reconstructing imagesgenerated based on the temporally offset projection data to generate oneor more temporally offset images; and combining the temporally offsetimages.
 6. The image reconstruction method of claim 5, wherein thecombining the temporally offset images comprises using one or more ofaveraging, addition, weighted combination, or Fourier combination. 7.The image reconstruction method of claim 4, comprising: jointlyreconstructing images generated based on the temporally offsetprojection data to generate one or more temporally offset images; andcombining the temporally offset images.
 8. The image reconstructionmethod of claim 7, wherein the combining the temporally offset imagescomprises using one or more of averaging, addition, weightedcombination, or Fourier combination.
 9. The image reconstruction methodof claim 1, comprising rebinning the projection data to a parallel beamgeometry.
 10. A system comprising: a memory structure encoding one ormore processor-executable routines wherein the routines, when executedcause acts to be performed comprising: accessing projection dataacquired using a computed tomography system; computing adetector-channel dependent projection-domain weighting corresponding tothe projection data such that the contribution from one or more detectorregions is reduced, wherein the detector channel dependentprojection-domain weighting is view independent; applying thedetector-channel dependent projection-domain weighting to iterativelyreconstruct one or more images; and causing a display to display one ormore reconstructed images generated via iterative reconstruction withthe applied detector-channel dependent projection-domain weighting; anda processing component configured to access and execute the one or moreroutines encoded by the memory structure.
 11. The system of claim 10,wherein a weight of the detector-channel dependent projection-domainweighting has a value of 0 for one or more detector channels and a valueof 1 for one or more detector channels.
 12. The system of claim 11,wherein the weight of the detector-channel dependent projection-domainweighting has a value between 0 and 1 for one or more detector channels.13. The system of claim 10, wherein the detector-channel dependentprojection domain weighting comprises a Tam window or modified Tamwindow applied as a detector weighting function.
 14. The system of claim10, wherein the projection data comprise temporally offset projectiondata.
 15. The system of claim 14, wherein the routines, when executed bythe processor, cause further acts to be performed comprising:independently reconstructing images generated based on the temporallyoffset projection data to generate one or more temporally offset images;and combining the temporally offset images using one or more ofaveraging, addition, weighted combination, or Fourier combination. 16.The system of claim 14, wherein the routines, when executed by theprocessor, cause further acts to be performed comprising: jointlyreconstructing images generated based on the temporally offsetprojection data to generate one or more temporally offset images; andcombining the temporally offset images using one or more of averaging,addition, weighted combination, or Fourier combination.
 17. An imagereconstruction method, comprising the acts of: accessing one or moresets of projection data acquired using a computed tomography system;computing a detector-channel dependent projection-domain weightingcorresponding to the one or more sets of projection data; and applyingthe detector-channel dependent projection-domain weighting toiteratively reconstruct one or more images, wherein the projectiondomain weighting comprises a Tam window or modified Tam window appliedas a detector weighting function.
 18. The image reconstruction method ofclaim 17, wherein the detector weighting function applies a weightingfactor is the same across all of the projection data.
 19. The imagereconstruction method of claim 17, wherein the projection data comprisetemporally offset projection data, and the image reconstruction methodcomprises: independently reconstructing images generated based on thetemporally offset projection data to generate one or more temporallyoffset images; and combining the temporally offset images using one ormore of averaging, addition, weighted combination, or Fouriercombination.
 20. The image reconstruction method of claim 16, whereinthe projection data comprise temporally offset projection data, and theimage reconstruction method comprises: jointly reconstructing imagesgenerated based on the temporally offset projection data to generate oneor more temporally offset images; and combining the temporally offsetimages using one or more of averaging, addition, weighted combination,or Fourier combination.